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Micromechanical Prediction Model of Viscoelastic Properties for Asphalt Mastic Based on Morphologically Representative Pattern Approach

Authors :
Shuang Wang
Zhichen Wang
Naisheng Guo
Xu Yang
Source :
Advances in Materials Science and Engineering, Vol 2020 (2020)
Publication Year :
2020
Publisher :
Hindawi Limited, 2020.

Abstract

This paper is devoted to the introduction of physicochemical, filler size, and distribution effect in micromechanical predictions of the overall viscoelastic properties of asphalt mastic. In order to account for the three effects, the morphologically representative pattern (MRP) approach was employed. The MRP model was improved due to the arduous practical use of equivalent modulus formula solution. Then, a homogeneous morphologically representative model (H-MRP) with the explicit solution was established based on the homogenization theory. Asphalt mastic is regarded as a composite material consisting of filler particles coated structural asphalt and free asphalt considering the physicochemical effect. An additional interphase surrounding particles was introduced in the H-MRP model. Thus, a modified H-MRP model was established. Using the proposed model, a viscoelastic equation was derived to predict the complex modulus and subsequently the dynamic modulus of asphalt mastic based on the elastic-viscoelastic correspondence principle. The dynamic shear rheological tests were conducted to verify the prediction model. The results show that the predicted modulus presents an acceptable precision for asphalt mastic mixed with 10% and 20% fillers volume fraction, as compared to the measured ones. The predicted modulus agrees reasonably well with the measured ones at high frequencies for asphalt mastic mixed with 30% and 40% fillers volume fraction. However, it exhibits underestimated modulus at low frequencies. The reasons for the discrepancy between predicted and measured dynamic shear modulus and the factors affecting the dynamic shear modulus were also explored in the paper.

Details

Language :
English
ISSN :
16878442 and 16878434
Volume :
2020
Database :
OpenAIRE
Journal :
Advances in Materials Science and Engineering
Accession number :
edsair.doi.dedup.....c06bda104a16ee786fad5be0e26664c8